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Items: 1 to 20 of 27

1.

Disease networks and their contribution to disease understanding: A systematized review of their evolution, techniques and data sources.

García Del Valle EP, Lagunes García G, Prieto Santamaría L, Zanin M, Menasalvas Ruiz E, Rodríguez-González A.

J Biomed Inform. 2019 May 8:103206. doi: 10.1016/j.jbi.2019.103206. [Epub ahead of print] Review.

PMID:
31077818
2.

Indoor Temperature Prediction in an IoT Scenario.

Monteiro PL, Zanin M, Ruiz EM, Pimentão J, Sousa PADC.

Sensors (Basel). 2018 Oct 24;18(11). pii: E3610. doi: 10.3390/s18113610.

3.

A comparative analysis of approaches to network-dismantling.

Wandelt S, Sun X, Feng D, Zanin M, Havlin S.

Sci Rep. 2018 Sep 10;8(1):13513. doi: 10.1038/s41598-018-31902-8.

4.

Understanding diseases as increased heterogeneity: a complex network computational framework.

Zanin M, Tuñas JM, Menasalvas E.

J R Soc Interface. 2018 Aug;15(145). pii: 20180405. doi: 10.1098/rsif.2018.0405.

PMID:
30111665
5.

Topological structures are consistently overestimated in functional complex networks.

Zanin M, Belkoura S, Gomez J, Alfaro C, Cano J.

Sci Rep. 2018 Aug 10;8(1):11980. doi: 10.1038/s41598-018-30472-z.

6.

Profiling Lung Cancer Patients Using Electronic Health Records.

Menasalvas Ruiz E, Tuñas JM, Bermejo G, Gonzalo Martín C, Rodríguez-González A, Zanin M, González de Pedro C, Méndez M, Zaretskaia O, Rey J, Parejo C, Cruz Bermudez JL, Provencio M.

J Med Syst. 2018 May 31;42(7):126. doi: 10.1007/s10916-018-0975-9.

PMID:
29855732
7.

Community effort endorsing multiscale modelling, multiscale data science and multiscale computing for systems medicine.

Zanin M, Chorbev I, Stres B, Stalidzans E, Vera J, Tieri P, Castiglione F, Groen D, Zheng H, Baumbach J, Schmid JA, Basilio J, Klimek P, Debeljak N, Rozman D, Schmidt HHHW.

Brief Bioinform. 2017 Dec 5. doi: 10.1093/bib/bbx160. [Epub ahead of print]

PMID:
29220509
8.

Detecting switching and intermittent causalities in time series.

Zanin M, Papo D.

Chaos. 2017 Apr;27(4):047403. doi: 10.1063/1.4979046.

PMID:
28456157
9.

Using complex networks for refining survival prognosis in prostate cancer patient.

Zanin M.

F1000Res. 2016 Nov 16;5:2675. doi: 10.12688/f1000research.8282.1. eCollection 2016.

10.

The ACE Brain.

Zanin M, Papo D.

Front Comput Neurosci. 2016 Nov 25;10:122. eCollection 2016. No abstract available.

11.

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data.

Guinney J, Wang T, Laajala TD, Winner KK, Bare JC, Neto EC, Khan SA, Peddinti G, Airola A, Pahikkala T, Mirtti T, Yu T, Bot BM, Shen L, Abdallah K, Norman T, Friend S, Stolovitzky G, Soule H, Sweeney CJ, Ryan CJ, Scher HI, Sartor O, Xie Y, Aittokallio T, Zhou FL, Costello JC; Prostate Cancer Challenge DREAM Community.

Lancet Oncol. 2017 Jan;18(1):132-142. doi: 10.1016/S1470-2045(16)30560-5. Epub 2016 Nov 16.

12.

On causality of extreme events.

Zanin M.

PeerJ. 2016 Jun 7;4:e2111. doi: 10.7717/peerj.2111. eCollection 2016.

13.

Beware of the Small-World Neuroscientist!

Papo D, Zanin M, Martínez JH, Buldú JM.

Front Hum Neurosci. 2016 Mar 8;10:96. doi: 10.3389/fnhum.2016.00096. eCollection 2016. No abstract available.

14.

From phenotype to genotype in complex brain networks.

Zanin M, Correia M, Sousa PA, Cruz J.

Sci Rep. 2016 Jan 22;6:19790. doi: 10.1038/srep19790.

15.

Functional brain networks: great expectations, hard times and the big leap forward.

Papo D, Zanin M, Pineda-Pardo JA, Boccaletti S, Buldú JM.

Philos Trans R Soc Lond B Biol Sci. 2014 Oct 5;369(1653). pii: 20130525. doi: 10.1098/rstb.2013.0525.

16.

Parenclitic networks: uncovering new functions in biological data.

Zanin M, Alcazar JM, Carbajosa JV, Paez MG, Papo D, Sousa P, Menasalvas E, Boccaletti S.

Sci Rep. 2014 May 29;4:5112. doi: 10.1038/srep05112.

17.

Reconstructing functional brain networks: have we got the basics right?

Papo D, Zanin M, Buldú JM.

Front Hum Neurosci. 2014 Feb 27;8:107. doi: 10.3389/fnhum.2014.00107. eCollection 2014. No abstract available.

18.

Efficient neural codes can lead to spurious synchronization.

Zanin M, Papo D.

Front Comput Neurosci. 2013 Sep 10;7:125. doi: 10.3389/fncom.2013.00125. eCollection 2013. No abstract available.

19.

Feature selection in the reconstruction of complex network representations of spectral data.

Zanin M, Menasalvas E, Boccaletti S, Sousa P.

PLoS One. 2013 Aug 26;8(8):e72045. doi: 10.1371/journal.pone.0072045. eCollection 2013.

20.

Emergence of network features from multiplexity.

Cardillo A, Gómez-Gardeñes J, Zanin M, Romance M, Papo D, del Pozo F, Boccaletti S.

Sci Rep. 2013;3:1344. doi: 10.1038/srep01344.

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